Anyone have any guidance on how to use Fast.ai or other deep learning library/techniques to extract some specific information from text?
I have a clinical note (text written by a medical professional after a procedure) and want to extract some specific elements from to create a structured version of a report to be stored in a database.
In the courses (at least the parts I’ve completed), the examples working with text are all some sort of classification task.
As an example of a note, here is one from http://www.mtsamplereports.com/colonoscopy-medical-transcription-sample-reports/ - part of which is quoted here:
… The visualized mucosa in the cecum appeared grossly normal. In the proximal ascending colon, a 3 mm polyp was noted and removed in entirety with cold forceps biopsy. Remainder of the visualized mucosa in the ascending colon appeared grossly normal. In the mid transverse colon, a 2 mm polyp was noted and removed in entirety with cold forceps biopsy. In the distal transverse colon, a 7 mm polyp was noted and removed with a cold snare technique. The polyp specimen was easily retrieved. In the splenic flexure, a 1 cm lipoma was noted.
POSTOPERATIVE RECOMMENDATIONS: We will follow up on the biopsy results. If the colon polyps return as adenomatous, the patient will need a repeat colonoscopy in approximately three years.
In my case, I want to retrieve information such as number of polyps, size of polyps, recommendation for follow-up. Some clinical notes also have demographic information such as gender, age, reason for exam (family history, test results, etc) that I want to extract.
@srmsoumya had posted a similar question on a different NLP domain back in 2018 - Extracting specific information from documents: NLP - However the only reply was guidance on how to get text from a document, not actually extract the desired information.